Literature DB >> 31946725

3D Reconstruction of Whole Stomach from Endoscope Video Using Structure-from-Motion.

Aji Resindra Widya, Yusuke Monno, Kosuke Imahori, Masatoshi Okutomi, Sho Suzuki, Takuji Gotoda, Kenji Miki.   

Abstract

Gastric endoscopy is a common clinical practice that enables medical doctors to diagnose the stomach inside a body. In order to identify a gastric lesion's location such as early gastric cancer within the stomach, this work addressed to reconstruct the 3D shape of a whole stomach with color texture information generated from a standard monocular endoscope video. Previous works have tried to reconstruct the 3D structures of various organs from endoscope images. However, they are mainly focused on a partial surface. In this work, we investigated how to enable structure-from-motion (SfM) to reconstruct the whole shape of a stomach from a standard endoscope video. We specifically investigated the combined effect of chromo-endoscopy and color channel selection on SfM. Our study found that 3D reconstruction of the whole stomach can be achieved by using red channel images captured under chromo-endoscopy by spreading indigo carmine (IC) dye on the stomach surface.

Entities:  

Year:  2019        PMID: 31946725     DOI: 10.1109/EMBC.2019.8857964

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  3 in total

1.  Joint estimation of depth and motion from a monocular endoscopy image sequence using a multi-loss rebalancing network.

Authors:  Shiyuan Liu; Jingfan Fan; Dengpan Song; Tianyu Fu; Yucong Lin; Deqiang Xiao; Hong Song; Yongtian Wang; Jian Yang
Journal:  Biomed Opt Express       Date:  2022-04-11       Impact factor: 3.562

Review 2.  Deep learning for gastroscopic images: computer-aided techniques for clinicians.

Authors:  Ziyi Jin; Tianyuan Gan; Peng Wang; Zuoming Fu; Chongan Zhang; Qinglai Yan; Xueyong Zheng; Xiao Liang; Xuesong Ye
Journal:  Biomed Eng Online       Date:  2022-02-11       Impact factor: 2.819

3.  Stomach 3D Reconstruction Using Virtual Chromoendoscopic Images.

Authors:  Aji Resindra Widya; Yusuke Monno; Masatoshi Okutomi; Sho Suzuki; Takuji Gotoda; Kenji Miki
Journal:  IEEE J Transl Eng Health Med       Date:  2021-02-24       Impact factor: 3.316

  3 in total

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